An Efficient Game-Theoretic Planner for Automated Lane Merging with Multi-Modal Behavior Understanding
Luyao Zhang (TU Delft - Team Sergio Grammatico)
S Han (Student TU Delft)
Sergio Grammatico (TU Delft - Team Bart De Schutter, TU Delft - Team Sergio Grammatico)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
In this paper, we propose a novel behavior planner that combines game
theory with search-based planning for automated lane merging.
Specifically, inspired by human drivers, we model the interaction
between vehicles as a gap selection process. To overcome the challenge
of multi-modal behavior exhibited by the surrounding vehicles, we
formulate the trajectory selection as a matrix game and compute an
equilibrium. Next, we validate our proposed planner in the high-fidelity
simulator CARLA and demonstrate its effectiveness in handling
interactions in dense traffic scenarios.